{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a09b627",
    "execution_start": 1627775236550,
    "execution_millis": 2229,
    "cell_id": "00000-0910803a-0a49-4c33-90bb-ae33d07e78bc",
    "deepnote_cell_type": "code"
   },
   "source": "!! pip install pingouin",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 1,
     "data": {
      "text/plain": "['Requirement already satisfied: pingouin in /root/venv/lib/python3.7/site-packages (0.3.12)',\n 'Requirement already satisfied: pandas>=0.24 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.2.5)',\n 'Requirement already satisfied: statsmodels>=0.10.0 in /root/venv/lib/python3.7/site-packages (from pingouin) (0.12.2)',\n 'Requirement already satisfied: outdated in /root/venv/lib/python3.7/site-packages (from pingouin) (0.2.1)',\n 'Requirement already satisfied: matplotlib>=3.0.2 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (3.4.2)',\n 'Requirement already satisfied: seaborn>=0.9.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.11.1)',\n 'Requirement already satisfied: numpy>=1.15 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.19.5)',\n 'Requirement already satisfied: scikit-learn in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.24.2)',\n 'Requirement already satisfied: scipy>=1.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (1.7.0)',\n 'Requirement already satisfied: tabulate in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pingouin) (0.8.9)',\n 'Requirement already satisfied: pandas-flavor>=0.1.2 in /root/venv/lib/python3.7/site-packages (from pingouin) (0.2.0)',\n 'Requirement already satisfied: pillow>=6.2.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (8.3.1)',\n 'Requirement already satisfied: python-dateutil>=2.7 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (2.8.2)',\n 'Requirement already satisfied: kiwisolver>=1.0.1 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (1.3.1)',\n 'Requirement already satisfied: cycler>=0.10 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)',\n 'Requirement already satisfied: pyparsing>=2.2.1 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)',\n 'Requirement already satisfied: six in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from cycler>=0.10->matplotlib>=3.0.2->pingouin) (1.16.0)',\n 'Requirement already satisfied: pytz>=2017.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas>=0.24->pingouin) (2021.1)',\n 'Requirement already satisfied: xarray in /root/venv/lib/python3.7/site-packages (from pandas-flavor>=0.1.2->pingouin) (0.19.0)',\n 'Requirement already satisfied: patsy>=0.5 in /root/venv/lib/python3.7/site-packages (from statsmodels>=0.10.0->pingouin) (0.5.1)',\n 'Requirement already satisfied: littleutils in /root/venv/lib/python3.7/site-packages (from outdated->pingouin) (0.2.2)',\n 'Requirement already satisfied: requests in /shared-libs/python3.7/py/lib/python3.7/site-packages (from outdated->pingouin) (2.26.0)',\n 'Requirement already satisfied: charset-normalizer~=2.0.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (2.0.3)',\n 'Requirement already satisfied: urllib3<1.27,>=1.21.1 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (1.26.6)',\n 'Requirement already satisfied: certifi>=2017.4.17 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (2021.5.30)',\n 'Requirement already satisfied: idna<4,>=2.5 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from requests->outdated->pingouin) (3.2)',\n 'Requirement already satisfied: joblib>=0.11 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn->pingouin) (1.0.1)',\n 'Requirement already satisfied: threadpoolctl>=2.0.0 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from scikit-learn->pingouin) (2.2.0)',\n 'Requirement already satisfied: setuptools>=40.4 in /root/venv/lib/python3.7/site-packages (from xarray->pandas-flavor>=0.1.2->pingouin) (57.1.0)',\n 'WARNING: You are using pip version 21.1.3; however, version 21.2.2 is available.',\n \"You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.\"]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c20757a8",
    "execution_start": 1627775238781,
    "execution_millis": 1396,
    "cell_id": "00001-0765220f-aa58-4329-a847-adcbe1c3652e",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nimport scipy\nfrom scipy import stats\nfrom sklearn import datasets\n\nimport pingouin as pg\n\nfrom numpy import mean\nfrom numpy import std\nfrom numpy.random import randn\nfrom numpy.random import seed\nfrom matplotlib import pyplot\n\n# seed random number generator\nseed(1)\n\n# read data from CSV\ndf = pd.read_csv(\"Data_raw.csv\")\ndf1 = df[df['Group'] == \"Ahmad\"] \ndf2 = df[df['Group'] != \"Ahmad\"] ",
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f804c160",
    "execution_start": 1627775240185,
    "execution_millis": 33,
    "cell_id": "00002-9978f322-e82d-4f1b-a0ed-f731c687e38a",
    "deepnote_cell_type": "code"
   },
   "source": "df",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 3,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 34,
       "column_count": 3,
       "columns": [
        {
         "name": "vitd",
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         "stats": {
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          ]
         }
        },
        {
         "name": "mort",
         "dtype": "float64",
         "stats": {
          "unique_count": 32,
          "nan_count": 0,
          "min": "10.0",
          "max": "178.4",
          "histogram": [
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        },
        {
         "name": "Group",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
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           {
            "name": "Ahmad",
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           {
            "name": "Hospital studies",
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          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
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       ],
       "rows_top": [
        {
         "vitd": 15.6,
         "mort": 97.3,
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         "vitd": 17,
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        {
         "vitd": 22.4,
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        {
         "vitd": 22.56,
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        {
         "vitd": 23.18,
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        {
         "vitd": 23.8,
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        {
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        {
         "vitd": 27.08,
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        {
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        },
        {
         "vitd": 32.6,
         "mort": 71.3,
         "Group": "Ahmad",
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        },
        {
         "vitd": 16.8,
         "mort": 59.8,
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        },
        {
         "vitd": 39,
         "mort": 17.9,
         "Group": "Hospital studies",
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        },
        {
         "vitd": 16,
         "mort": 53.4,
         "Group": "Hospital studies",
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        },
        {
         "vitd": 32,
         "mort": 44.3,
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        },
        {
         "vitd": 12.77,
         "mort": 143.7,
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         "vitd": 30.79,
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        {
         "vitd": 15,
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        },
        {
         "vitd": 25,
         "mort": 39.5,
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        },
        {
         "vitd": 28.4,
         "mort": 87.9,
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        },
        {
         "vitd": 8,
         "mort": 178.4,
         "Group": "Hospital studies",
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        },
        {
         "vitd": 14,
         "mort": 48.9,
         "Group": "Hospital studies",
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        },
        {
         "vitd": 31,
         "mort": 36.1,
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        },
        {
         "vitd": 13.41,
         "mort": 19.1,
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        },
        {
         "vitd": 23.19,
         "mort": 10,
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        },
        {
         "vitd": 32.98,
         "mort": 37.7,
         "Group": "Hospital studies",
         "_deepnote_index_column": 33
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "     vitd   mort             Group\n0   15.60   97.3             Ahmad\n1   17.00  118.6             Ahmad\n2   18.40  104.4             Ahmad\n3   18.96  141.7             Ahmad\n4   19.72  178.4             Ahmad\n5   20.00  140.0             Ahmad\n6   20.04   61.7             Ahmad\n7   22.40   81.4             Ahmad\n8   22.56   58.1             Ahmad\n9   23.18   53.7             Ahmad\n10  23.80   79.0             Ahmad\n11  24.00  111.5             Ahmad\n12  24.24  122.3             Ahmad\n13  25.00  141.3             Ahmad\n14  26.00   33.5             Ahmad\n15  26.00   10.0             Ahmad\n16  27.08   11.6             Ahmad\n17  29.40  109.0             Ahmad\n18  32.60   71.3             Ahmad\n19  16.80   59.8  Hospital studies\n20  39.00   17.9  Hospital studies\n21  16.00   53.4  Hospital studies\n22  32.00   44.3  Hospital studies\n23  12.77  143.7  Hospital studies\n24  30.79   78.0  Hospital studies\n25  15.00   49.0  Hospital studies\n26  25.00   39.5  Hospital studies\n27  28.40   87.9  Hospital studies\n28   8.00  178.4  Hospital studies\n29  14.00   48.9  Hospital studies\n30  31.00   36.1  Hospital studies\n31  13.41   19.1  Hospital studies\n32  23.19   10.0  Hospital studies\n33  32.98   37.7  Hospital studies",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>vitd</th>\n      <th>mort</th>\n      <th>Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.60</td>\n      <td>97.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17.00</td>\n      <td>118.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.40</td>\n      <td>104.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18.96</td>\n      <td>141.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>19.72</td>\n      <td>178.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20.00</td>\n      <td>140.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20.04</td>\n      <td>61.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>22.40</td>\n      <td>81.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>22.56</td>\n      <td>58.1</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>23.18</td>\n      <td>53.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>23.80</td>\n      <td>79.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>24.00</td>\n      <td>111.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>24.24</td>\n      <td>122.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>25.00</td>\n      <td>141.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>26.00</td>\n      <td>33.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>26.00</td>\n      <td>10.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>27.08</td>\n      <td>11.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>29.40</td>\n      <td>109.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>32.60</td>\n      <td>71.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>16.80</td>\n      <td>59.8</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>39.00</td>\n      <td>17.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>16.00</td>\n      <td>53.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>32.00</td>\n      <td>44.3</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>12.77</td>\n      <td>143.7</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>30.79</td>\n      <td>78.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>15.00</td>\n      <td>49.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>25.00</td>\n      <td>39.5</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28.40</td>\n      <td>87.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>8.00</td>\n      <td>178.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>14.00</td>\n      <td>48.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>31.00</td>\n      <td>36.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>13.41</td>\n      <td>19.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>23.19</td>\n      <td>10.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>32.98</td>\n      <td>37.7</td>\n      <td>Hospital studies</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:35:22.724114Z",
     "start_time": "2021-03-18T17:35:21.632259Z"
    },
    "deepnote_to_be_reexecuted": false,
    "source_hash": "487e9808",
    "execution_start": 1627775240224,
    "execution_millis": 3,
    "cell_id": "00003-3cc23271-59a8-45f5-9115-7e4b48128db8",
    "deepnote_cell_type": "code"
   },
   "source": "# Common imports\nimport numpy as np # numpy is THE toolbox for scientific computing with python\nimport pandas as pd # pandas provides THE data structure and data analysis tools for data scientists \n\n# maximum number of columns\npd.set_option(\"display.max_rows\", 101)\npd.set_option(\"display.max_columns\", 101)\n\n# To plot pretty figures\n%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\nfrom warnings import filterwarnings\nfilterwarnings('ignore')",
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-25T17:06:00.173275Z",
     "start_time": "2020-08-25T17:06:00.151315Z"
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   "source": "df",
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    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 34,
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        },
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        },
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        },
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        {
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        },
        {
         "vitd": 28.4,
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        },
        {
         "vitd": 8,
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         "Group": "Hospital studies",
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        },
        {
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        },
        {
         "vitd": 31,
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         "Group": "Hospital studies",
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        },
        {
         "vitd": 13.41,
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        },
        {
         "vitd": 23.19,
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        },
        {
         "vitd": 32.98,
         "mort": 37.7,
         "Group": "Hospital studies",
         "_deepnote_index_column": 33
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "     vitd   mort             Group\n0   15.60   97.3             Ahmad\n1   17.00  118.6             Ahmad\n2   18.40  104.4             Ahmad\n3   18.96  141.7             Ahmad\n4   19.72  178.4             Ahmad\n5   20.00  140.0             Ahmad\n6   20.04   61.7             Ahmad\n7   22.40   81.4             Ahmad\n8   22.56   58.1             Ahmad\n9   23.18   53.7             Ahmad\n10  23.80   79.0             Ahmad\n11  24.00  111.5             Ahmad\n12  24.24  122.3             Ahmad\n13  25.00  141.3             Ahmad\n14  26.00   33.5             Ahmad\n15  26.00   10.0             Ahmad\n16  27.08   11.6             Ahmad\n17  29.40  109.0             Ahmad\n18  32.60   71.3             Ahmad\n19  16.80   59.8  Hospital studies\n20  39.00   17.9  Hospital studies\n21  16.00   53.4  Hospital studies\n22  32.00   44.3  Hospital studies\n23  12.77  143.7  Hospital studies\n24  30.79   78.0  Hospital studies\n25  15.00   49.0  Hospital studies\n26  25.00   39.5  Hospital studies\n27  28.40   87.9  Hospital studies\n28   8.00  178.4  Hospital studies\n29  14.00   48.9  Hospital studies\n30  31.00   36.1  Hospital studies\n31  13.41   19.1  Hospital studies\n32  23.19   10.0  Hospital studies\n33  32.98   37.7  Hospital studies",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>vitd</th>\n      <th>mort</th>\n      <th>Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.60</td>\n      <td>97.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17.00</td>\n      <td>118.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.40</td>\n      <td>104.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18.96</td>\n      <td>141.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>19.72</td>\n      <td>178.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20.00</td>\n      <td>140.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20.04</td>\n      <td>61.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>22.40</td>\n      <td>81.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>22.56</td>\n      <td>58.1</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>23.18</td>\n      <td>53.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>23.80</td>\n      <td>79.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>24.00</td>\n      <td>111.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>24.24</td>\n      <td>122.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>25.00</td>\n      <td>141.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>26.00</td>\n      <td>33.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>26.00</td>\n      <td>10.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>27.08</td>\n      <td>11.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>29.40</td>\n      <td>109.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>32.60</td>\n      <td>71.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>16.80</td>\n      <td>59.8</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>39.00</td>\n      <td>17.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>16.00</td>\n      <td>53.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>32.00</td>\n      <td>44.3</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>12.77</td>\n      <td>143.7</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>30.79</td>\n      <td>78.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>15.00</td>\n      <td>49.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>25.00</td>\n      <td>39.5</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28.40</td>\n      <td>87.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>8.00</td>\n      <td>178.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>14.00</td>\n      <td>48.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>31.00</td>\n      <td>36.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>13.41</td>\n      <td>19.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>23.19</td>\n      <td>10.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>32.98</td>\n      <td>37.7</td>\n      <td>Hospital studies</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "98aa29c8",
    "execution_start": 1627775240273,
    "execution_millis": 390,
    "cell_id": "00005-41ec9a0d-1f7e-4b4e-bd37-2095086a5b23",
    "deepnote_cell_type": "code"
   },
   "source": "import seaborn as sns\nfrom matplotlib.lines import Line2D\n\nsns.set_style('white')\nsns.set_context('paper', font_scale=2)\nsns.set_style('ticks')\nsns.set_theme(color_codes=True)\n#fig = plt.style.use('fivethirtyeight')\n\nfig, main_ax = plt.subplots(figsize=(15, 8))\n#main_ax.set(xlim=(0,50))\n#main_ax.set(ylim=(0,63))\n#sns.regplot(x=\"vitd\", y=\"mort\", data=df, truncate=False, ax=main_ax)\nscat1 = sns.lineplot(x = 'vitd', y = 'mort', data = df1, ci = 95, marker=\"o\", ax=main_ax)\nscat2 = sns.lineplot(x = 'vitd', y = 'mort', data = df2, ci = 95, marker=\"x\", ax=main_ax)\n\nmain_ax.legend([scat1.scatter(df1.iloc[0][\"vitd\"],df1.iloc[0][\"mort\"]), scat2.scatter(df2.iloc[0][\"vitd\"],df2.iloc[0][\"mort\"])], [df1.iloc[0][\"Group\"], df2.iloc[0][\"Group\"]+\" (corrected)\"])\n\nplt.xlabel(\"Vitamin D [ng/mL]\")\nplt.ylabel(\"Mortality coefficient\")\nplt.show(fig)",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 898,
       "height": 485
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "61981b20",
    "execution_start": 1627775240669,
    "execution_millis": 597,
    "cell_id": "00006-0e37c239-6e9c-4115-9c7d-c168cc1761f5",
    "deepnote_cell_type": "code"
   },
   "source": "import seaborn as sns\nfrom matplotlib.lines import Line2D\n\nsns.set_style('white')\nsns.set_context('paper', font_scale=2)\nsns.set_style('ticks')\nsns.set_theme(color_codes=True)\n#fig = plt.style.use('fivethirtyeight')\n\nfig, main_ax = plt.subplots(figsize=(15, 8))\nmain_ax.set(xlim=(0,50))\nmain_ax.set(ylim=(0,200))\n#sns.regplot(x=\"vitd\", y=\"mort\", data=df, truncate=False, ax=main_ax)\nscat1 = sns.regplot(x = 'vitd', y = 'mort', data = df1, ci = 5, marker=\"o\", truncate=False, ax=main_ax)\nscat2 = sns.regplot(x = 'vitd', y = 'mort', data = df2, ci = 5, marker=\"x\", truncate=False, ax=main_ax)\n\nmain_ax.legend([scat1.scatter(df1.iloc[0][\"vitd\"],df1.iloc[0][\"mort\"]), scat2.scatter(df2.iloc[0][\"vitd\"],df2.iloc[0][\"mort\"])], [df1.iloc[0][\"Group\"], df2.iloc[0][\"Group\"]+\" (corrected)\"])\n\nplt.xlabel(\"Vitamin D [ng/mL]\")\nplt.ylabel(\"Mortality coefficient\")\nplt.show(fig)",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 905,
       "height": 489
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "10deec4a",
    "execution_start": 1627775241262,
    "execution_millis": 340,
    "cell_id": "00007-62308874-c664-46d2-9f21-bc4535016e66",
    "deepnote_cell_type": "code"
   },
   "source": "import seaborn as sns\nfrom matplotlib.lines import Line2D\n\nsns.set_style('white')\nsns.set_context('paper', font_scale=2)\nsns.set_style('ticks')\nsns.set_theme(color_codes=True)\n#fig = plt.style.use('fivethirtyeight')\n\nfig, main_ax = plt.subplots(figsize=(15, 8))\n#main_ax.set(xlim=(0,50))\n#main_ax.set(ylim=(0,63))\nscat1 = sns.regplot(x = 'vitd', y = 'mort', data = df, ci = 10, truncate=False, ax=main_ax)\n\nplt.xlabel(\"Vitamin D [ng/mL]\")\nplt.ylabel(\"Mortality coefficient\")\nplt.show(fig)",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 898,
       "height": 485
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5c8b9c28",
    "execution_start": 1627775241604,
    "execution_millis": 263,
    "cell_id": "00008-10045929-91c8-4d00-93e0-dfed971b83b0",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport statsmodels.api as sm\nfrom statsmodels.sandbox.regression.predstd import wls_prediction_std\n\ndef ols(arr):\n    model = sm.formula.ols(formula='mort ~ vitd', data=arr)\n    results = model.fit()\n    print(results.summary())\n\nols(df)\nols(df1)\nols(df2)",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "text": "                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                   mort   R-squared:                       0.159\nModel:                            OLS   Adj. R-squared:                  0.133\nMethod:                 Least Squares   F-statistic:                     6.056\nDate:                Sat, 31 Jul 2021   Prob (F-statistic):             0.0194\nTime:                        23:47:21   Log-Likelihood:                -176.38\nNo. Observations:                  34   AIC:                             356.8\nDf Residuals:                      32   BIC:                             359.8\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept    140.2880     26.712      5.252      0.000      85.877     194.699\nvitd          -2.7654      1.124     -2.461      0.019      -5.054      -0.477\n==============================================================================\nOmnibus:                        1.661   Durbin-Watson:                   1.217\nProb(Omnibus):                  0.436   Jarque-Bera (JB):                1.117\nSkew:                           0.127   Prob(JB):                        0.572\nKurtosis:                       2.149   Cond. No.                         83.1\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                   mort   R-squared:                       0.173\nModel:                            OLS   Adj. R-squared:                  0.124\nMethod:                 Least Squares   F-statistic:                     3.545\nDate:                Sat, 31 Jul 2021   Prob (F-statistic):             0.0770\nTime:                        23:47:21   Log-Likelihood:                -97.362\nNo. Observations:                  19   AIC:                             198.7\nDf Residuals:                      17   BIC:                             200.6\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept    192.6788     55.013      3.502      0.003      76.612     308.746\nvitd          -4.4408      2.359     -1.883      0.077      -9.417       0.535\n==============================================================================\nOmnibus:                        2.146   Durbin-Watson:                   1.238\nProb(Omnibus):                  0.342   Jarque-Bera (JB):                1.052\nSkew:                           0.062   Prob(JB):                        0.591\nKurtosis:                       1.854   Cond. No.                         130.\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                   mort   R-squared:                       0.239\nModel:                            OLS   Adj. R-squared:                  0.180\nMethod:                 Least Squares   F-statistic:                     4.076\nDate:                Sat, 31 Jul 2021   Prob (F-statistic):             0.0646\nTime:                        23:47:21   Log-Likelihood:                -76.269\nNo. Observations:                  15   AIC:                             156.5\nDf Residuals:                      13   BIC:                             158.0\nDf Model:                           1                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nIntercept    114.4156     28.937      3.954      0.002      51.902     176.930\nvitd          -2.4015      1.189     -2.019      0.065      -4.971       0.168\n==============================================================================\nOmnibus:                        1.130   Durbin-Watson:                   1.514\nProb(Omnibus):                  0.568   Jarque-Bera (JB):                0.824\nSkew:                           0.530   Prob(JB):                        0.662\nKurtosis:                       2.557   Cond. No.                         65.0\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": "model = sm.formula.ols(formula='mort ~ vitd', data=df)\nresults = model.fit()\n-results.params.Intercept/results.params.vitd # y=intercept+vitd*x, solve for y=0",
   "metadata": {
    "tags": [],
    "cell_id": "00009-dcf82124-9037-4cf4-9045-1bb508ac1696",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "28ace1cf",
    "execution_start": 1627775383800,
    "execution_millis": 7,
    "deepnote_cell_type": "code"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "50.730034865923486"
     },
     "metadata": {}
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": true,
    "source_hash": "5d8909d4",
    "execution_start": 1627775207893,
    "execution_millis": 9,
    "cell_id": "00009-90215b41-6119-490b-ba33-2eaf53888a89",
    "deepnote_cell_type": "code"
   },
   "source": "def iqr(arr):\n    # iqr, median\n    q75, q25, q50 = np.percentile(arr.to_numpy(), [75, 25, 50])\n    print((q50,q25,q75))\n    \niqr(df[[\"vitd\"]])\niqr(df1[[\"vitd\"]])\niqr(df2[[\"vitd\"]])",
   "outputs": [
    {
     "name": "stdout",
     "text": "(23.185000000000002, 17.35, 26.81)\n(23.18, 19.86, 25.5)\n(23.19, 14.5, 30.895)\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": true,
    "source_hash": "b623e53d",
    "execution_start": 1627775207907,
    "execution_millis": 4,
    "cell_id": "00010-f40e622e-5b9e-4463-8417-8df328fef27e",
    "deepnote_cell_type": "code"
   },
   "source": "",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": true,
    "source_hash": "75bb83b3",
    "execution_start": 1627775207914,
    "execution_millis": 18,
    "cell_id": "00011-98b3ab1c-e65a-4297-bd5c-c897be5f070a",
    "deepnote_cell_type": "code"
   },
   "source": "def correlate(arr1, arr2):\n    print(scipy.stats.spearmanr(arr1, arr2, axis=0, nan_policy='propagate'))\n    print(scipy.stats.pearsonr(arr1, arr2))\n\ncorrelate(df[\"vitd\"], df[\"mort\"])\ncorrelate(df1[\"vitd\"], df1[\"mort\"])\ncorrelate(df2[\"vitd\"], df2[\"mort\"])",
   "outputs": [
    {
     "name": "stdout",
     "text": "SpearmanrResult(correlation=-0.3697845025217791, pvalue=0.03135821688182258)\n(-0.39892794709043417, 0.019434750125983877)\nSpearmanrResult(correlation=-0.43001320506473206, pvalue=0.06611844456588518)\n(-0.41538466742992153, 0.07695713714733686)\nSpearmanrResult(correlation=-0.4678571428571427, pvalue=0.0786302311543806)\n(-0.48857912295637485, 0.06460417875206008)\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": true,
    "source_hash": "f804c160",
    "execution_start": 1627775207930,
    "execution_millis": 33,
    "cell_id": "00012-366434f7-f97e-40d1-be74-ab0c937543b9",
    "deepnote_cell_type": "code"
   },
   "source": "df",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 34,
       "column_count": 3,
       "columns": [
        {
         "name": "vitd",
         "dtype": "float64",
         "stats": {
          "unique_count": 32,
          "nan_count": 0,
          "min": "8.0",
          "max": "39.0",
          "histogram": [
           {
            "bin_start": 8,
            "bin_end": 11.1,
            "count": 1
           },
           {
            "bin_start": 11.1,
            "bin_end": 14.2,
            "count": 3
           },
           {
            "bin_start": 14.2,
            "bin_end": 17.3,
            "count": 5
           },
           {
            "bin_start": 17.3,
            "bin_end": 20.4,
            "count": 5
           },
           {
            "bin_start": 20.4,
            "bin_end": 23.5,
            "count": 4
           },
           {
            "bin_start": 23.5,
            "bin_end": 26.6,
            "count": 7
           },
           {
            "bin_start": 26.6,
            "bin_end": 29.7,
            "count": 3
           },
           {
            "bin_start": 29.7,
            "bin_end": 32.8,
            "count": 4
           },
           {
            "bin_start": 32.8,
            "bin_end": 35.900000000000006,
            "count": 1
           },
           {
            "bin_start": 35.900000000000006,
            "bin_end": 39,
            "count": 1
           }
          ]
         }
        },
        {
         "name": "mort",
         "dtype": "float64",
         "stats": {
          "unique_count": 32,
          "nan_count": 0,
          "min": "10.0",
          "max": "178.4",
          "histogram": [
           {
            "bin_start": 10,
            "bin_end": 26.84,
            "count": 5
           },
           {
            "bin_start": 26.84,
            "bin_end": 43.68,
            "count": 4
           },
           {
            "bin_start": 43.68,
            "bin_end": 60.519999999999996,
            "count": 7
           },
           {
            "bin_start": 60.519999999999996,
            "bin_end": 77.36,
            "count": 2
           },
           {
            "bin_start": 77.36,
            "bin_end": 94.2,
            "count": 4
           },
           {
            "bin_start": 94.2,
            "bin_end": 111.03999999999999,
            "count": 3
           },
           {
            "bin_start": 111.03999999999999,
            "bin_end": 127.88,
            "count": 3
           },
           {
            "bin_start": 127.88,
            "bin_end": 144.72,
            "count": 4
           },
           {
            "bin_start": 144.72,
            "bin_end": 161.56,
            "count": 0
           },
           {
            "bin_start": 161.56,
            "bin_end": 178.4,
            "count": 2
           }
          ]
         }
        },
        {
         "name": "Group",
         "dtype": "object",
         "stats": {
          "unique_count": 2,
          "nan_count": 0,
          "categories": [
           {
            "name": "Ahmad",
            "count": 19
           },
           {
            "name": "Hospital studies",
            "count": 15
           }
          ]
         }
        },
        {
         "name": "_deepnote_index_column",
         "dtype": "int64"
        }
       ],
       "rows_top": [
        {
         "vitd": 15.6,
         "mort": 97.3,
         "Group": "Ahmad",
         "_deepnote_index_column": 0
        },
        {
         "vitd": 17,
         "mort": 118.6,
         "Group": "Ahmad",
         "_deepnote_index_column": 1
        },
        {
         "vitd": 18.4,
         "mort": 104.4,
         "Group": "Ahmad",
         "_deepnote_index_column": 2
        },
        {
         "vitd": 18.96,
         "mort": 141.7,
         "Group": "Ahmad",
         "_deepnote_index_column": 3
        },
        {
         "vitd": 19.72,
         "mort": 178.4,
         "Group": "Ahmad",
         "_deepnote_index_column": 4
        },
        {
         "vitd": 20,
         "mort": 140,
         "Group": "Ahmad",
         "_deepnote_index_column": 5
        },
        {
         "vitd": 20.04,
         "mort": 61.7,
         "Group": "Ahmad",
         "_deepnote_index_column": 6
        },
        {
         "vitd": 22.4,
         "mort": 81.4,
         "Group": "Ahmad",
         "_deepnote_index_column": 7
        },
        {
         "vitd": 22.56,
         "mort": 58.1,
         "Group": "Ahmad",
         "_deepnote_index_column": 8
        },
        {
         "vitd": 23.18,
         "mort": 53.7,
         "Group": "Ahmad",
         "_deepnote_index_column": 9
        },
        {
         "vitd": 23.8,
         "mort": 79,
         "Group": "Ahmad",
         "_deepnote_index_column": 10
        },
        {
         "vitd": 24,
         "mort": 111.5,
         "Group": "Ahmad",
         "_deepnote_index_column": 11
        },
        {
         "vitd": 24.24,
         "mort": 122.3,
         "Group": "Ahmad",
         "_deepnote_index_column": 12
        },
        {
         "vitd": 25,
         "mort": 141.3,
         "Group": "Ahmad",
         "_deepnote_index_column": 13
        },
        {
         "vitd": 26,
         "mort": 33.5,
         "Group": "Ahmad",
         "_deepnote_index_column": 14
        },
        {
         "vitd": 26,
         "mort": 10,
         "Group": "Ahmad",
         "_deepnote_index_column": 15
        },
        {
         "vitd": 27.08,
         "mort": 11.6,
         "Group": "Ahmad",
         "_deepnote_index_column": 16
        },
        {
         "vitd": 29.4,
         "mort": 109,
         "Group": "Ahmad",
         "_deepnote_index_column": 17
        },
        {
         "vitd": 32.6,
         "mort": 71.3,
         "Group": "Ahmad",
         "_deepnote_index_column": 18
        },
        {
         "vitd": 16.8,
         "mort": 59.8,
         "Group": "Hospital studies",
         "_deepnote_index_column": 19
        },
        {
         "vitd": 39,
         "mort": 17.9,
         "Group": "Hospital studies",
         "_deepnote_index_column": 20
        },
        {
         "vitd": 16,
         "mort": 53.4,
         "Group": "Hospital studies",
         "_deepnote_index_column": 21
        },
        {
         "vitd": 32,
         "mort": 44.3,
         "Group": "Hospital studies",
         "_deepnote_index_column": 22
        },
        {
         "vitd": 12.77,
         "mort": 143.7,
         "Group": "Hospital studies",
         "_deepnote_index_column": 23
        },
        {
         "vitd": 30.79,
         "mort": 78,
         "Group": "Hospital studies",
         "_deepnote_index_column": 24
        },
        {
         "vitd": 15,
         "mort": 49,
         "Group": "Hospital studies",
         "_deepnote_index_column": 25
        },
        {
         "vitd": 25,
         "mort": 39.5,
         "Group": "Hospital studies",
         "_deepnote_index_column": 26
        },
        {
         "vitd": 28.4,
         "mort": 87.9,
         "Group": "Hospital studies",
         "_deepnote_index_column": 27
        },
        {
         "vitd": 8,
         "mort": 178.4,
         "Group": "Hospital studies",
         "_deepnote_index_column": 28
        },
        {
         "vitd": 14,
         "mort": 48.9,
         "Group": "Hospital studies",
         "_deepnote_index_column": 29
        },
        {
         "vitd": 31,
         "mort": 36.1,
         "Group": "Hospital studies",
         "_deepnote_index_column": 30
        },
        {
         "vitd": 13.41,
         "mort": 19.1,
         "Group": "Hospital studies",
         "_deepnote_index_column": 31
        },
        {
         "vitd": 23.19,
         "mort": 10,
         "Group": "Hospital studies",
         "_deepnote_index_column": 32
        },
        {
         "vitd": 32.98,
         "mort": 37.7,
         "Group": "Hospital studies",
         "_deepnote_index_column": 33
        }
       ],
       "rows_bottom": null
      },
      "text/plain": "     vitd   mort             Group\n0   15.60   97.3             Ahmad\n1   17.00  118.6             Ahmad\n2   18.40  104.4             Ahmad\n3   18.96  141.7             Ahmad\n4   19.72  178.4             Ahmad\n5   20.00  140.0             Ahmad\n6   20.04   61.7             Ahmad\n7   22.40   81.4             Ahmad\n8   22.56   58.1             Ahmad\n9   23.18   53.7             Ahmad\n10  23.80   79.0             Ahmad\n11  24.00  111.5             Ahmad\n12  24.24  122.3             Ahmad\n13  25.00  141.3             Ahmad\n14  26.00   33.5             Ahmad\n15  26.00   10.0             Ahmad\n16  27.08   11.6             Ahmad\n17  29.40  109.0             Ahmad\n18  32.60   71.3             Ahmad\n19  16.80   59.8  Hospital studies\n20  39.00   17.9  Hospital studies\n21  16.00   53.4  Hospital studies\n22  32.00   44.3  Hospital studies\n23  12.77  143.7  Hospital studies\n24  30.79   78.0  Hospital studies\n25  15.00   49.0  Hospital studies\n26  25.00   39.5  Hospital studies\n27  28.40   87.9  Hospital studies\n28   8.00  178.4  Hospital studies\n29  14.00   48.9  Hospital studies\n30  31.00   36.1  Hospital studies\n31  13.41   19.1  Hospital studies\n32  23.19   10.0  Hospital studies\n33  32.98   37.7  Hospital studies",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>vitd</th>\n      <th>mort</th>\n      <th>Group</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15.60</td>\n      <td>97.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17.00</td>\n      <td>118.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>18.40</td>\n      <td>104.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>18.96</td>\n      <td>141.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>19.72</td>\n      <td>178.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20.00</td>\n      <td>140.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>20.04</td>\n      <td>61.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>22.40</td>\n      <td>81.4</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>22.56</td>\n      <td>58.1</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>23.18</td>\n      <td>53.7</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>23.80</td>\n      <td>79.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>24.00</td>\n      <td>111.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>24.24</td>\n      <td>122.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>25.00</td>\n      <td>141.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>26.00</td>\n      <td>33.5</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>26.00</td>\n      <td>10.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>27.08</td>\n      <td>11.6</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>29.40</td>\n      <td>109.0</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>32.60</td>\n      <td>71.3</td>\n      <td>Ahmad</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>16.80</td>\n      <td>59.8</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>39.00</td>\n      <td>17.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>16.00</td>\n      <td>53.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>32.00</td>\n      <td>44.3</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>12.77</td>\n      <td>143.7</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>30.79</td>\n      <td>78.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>15.00</td>\n      <td>49.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>25.00</td>\n      <td>39.5</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28.40</td>\n      <td>87.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>8.00</td>\n      <td>178.4</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>14.00</td>\n      <td>48.9</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>31.00</td>\n      <td>36.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>13.41</td>\n      <td>19.1</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>23.19</td>\n      <td>10.0</td>\n      <td>Hospital studies</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>32.98</td>\n      <td>37.7</td>\n      <td>Hospital studies</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-25T16:19:04.197048Z",
     "start_time": "2020-08-25T16:19:04.023008Z"
    },
    "deepnote_to_be_reexecuted": true,
    "source_hash": "6b70ba50",
    "execution_start": 1627775207973,
    "execution_millis": 342,
    "cell_id": "00013-40eb0459-de73-4908-b540-550455896d3c",
    "deepnote_cell_type": "code"
   },
   "source": "fig = sns.distplot(df.vitd)\nfig.set(ylabel='Density', xlabel='Vitamin D [ng/mL]')#, title='some title')",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "[Text(0, 0.5, 'Density'), Text(0.5, 0, 'Vitamin D [ng/mL]')]"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "image/png": {
       "width": 400,
       "height": 268
      }
     },
     "output_type": "display_data"
    }
   ],
   "execution_count": null
  },
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